
# Libraries to help with reading and manipulating data
import numpy as np
import pandas as pd

# Libraries to help with data visualization
import matplotlib.pyplot as plt
import seaborn as sns

# to scale the data using z-score
from sklearn.preprocessing import StandardScaler

# to compute distances
from scipy.spatial.distance import cdist

# to perform k-means clustering and compute silhouette scores
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score

# to visualize the elbow curve and silhouette scores
from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer

# to compute distances
from scipy.spatial.distance import pdist

# to perform hierarchical clustering, compute cophenetic correlation, and create dendrograms
from sklearn.cluster import AgglomerativeClustering
from scipy.cluster.hierarchy import dendrogram, linkage, cophenet

# to perform PCA
from sklearn.decomposition import PCA


import warnings
warnings.filterwarnings('ignore')


# loading the dataset
data = pd.read_csv("stock_data.csv")


# Make a copy of train dataset
df = data.copy()


pd.set_option('display.max_columns', None)

pd.set_option('display.max_rows', 200)


# viewing a random sample of the dataset
np.random.seed(1)
data.sample(200)


# check number of rows and columns
data.shape

(340, 15)


#check for missing data
data.isna().sum().sort_values(ascending=False)

Ticker Symbol                   0
Security                        0
GICS Sector                     0
GICS Sub Industry               0
Current Price                   0
Price Change                    0
Volatility                      0
ROE                             0
Cash Ratio                      0
Net Cash Flow                   0
Net Income                      0
Earnings Per Share              0
Estimated Shares Outstanding    0
P/E Ratio                       0
P/B Ratio                       0
dtype: int64


data[data.duplicated()].count()

Ticker Symbol                   0
Security                        0
GICS Sector                     0
GICS Sub Industry               0
Current Price                   0
Price Change                    0
Volatility                      0
ROE                             0
Cash Ratio                      0
Net Cash Flow                   0
Net Income                      0
Earnings Per Share              0
Estimated Shares Outstanding    0
P/E Ratio                       0
P/B Ratio                       0
dtype: int64


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 340 entries, 0 to 339
Data columns (total 15 columns):
 #   Column                        Non-Null Count  Dtype  
---  ------                        --------------  -----  
 0   Ticker Symbol                 340 non-null    object 
 1   Security                      340 non-null    object 
 2   GICS Sector                   340 non-null    object 
 3   GICS Sub Industry             340 non-null    object 
 4   Current Price                 340 non-null    float64
 5   Price Change                  340 non-null    float64
 6   Volatility                    340 non-null    float64
 7   ROE                           340 non-null    int64  
 8   Cash Ratio                    340 non-null    int64  
 9   Net Cash Flow                 340 non-null    int64  
 10  Net Income                    340 non-null    int64  
 11  Earnings Per Share            340 non-null    float64
 12  Estimated Shares Outstanding  340 non-null    float64
 13  P/E Ratio                     340 non-null    float64
 14  P/B Ratio                     340 non-null    float64
dtypes: float64(7), int64(4), object(4)
memory usage: 40.0+ KB


# get the column names that are object type
object_columns = list(data.select_dtypes(include=['object']).columns)
object_columns

['Ticker Symbol', 'Security', 'GICS Sector', 'GICS Sub Industry']


data.describe(include=['object']).T


data.groupby(['GICS Sector']).size().sort_values(ascending=False).reset_index()


data.groupby(['GICS Sector','GICS Sub Industry']).size().sort_values(ascending=False).reset_index()


# fixing column names
data.columns = [c.replace("/", "_") for c in data.columns]


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 340 entries, 0 to 339
Data columns (total 15 columns):
 #   Column                        Non-Null Count  Dtype  
---  ------                        --------------  -----  
 0   Ticker Symbol                 340 non-null    object 
 1   Security                      340 non-null    object 
 2   GICS Sector                   340 non-null    object 
 3   GICS Sub Industry             340 non-null    object 
 4   Current Price                 340 non-null    float64
 5   Price Change                  340 non-null    float64
 6   Volatility                    340 non-null    float64
 7   ROE                           340 non-null    int64  
 8   Cash Ratio                    340 non-null    int64  
 9   Net Cash Flow                 340 non-null    int64  
 10  Net Income                    340 non-null    int64  
 11  Earnings Per Share            340 non-null    float64
 12  Estimated Shares Outstanding  340 non-null    float64
 13  P_E Ratio                     340 non-null    float64
 14  P_B Ratio                     340 non-null    float64
dtypes: float64(7), int64(4), object(4)
memory usage: 40.0+ KB


# Let's look at the statistical summary of the data
data.describe().T


 #function to plot a boxplot and a histogram along the same scale.

# import the library for labelling
import matplotlib.patheffects as path_effects

# import the library for labelling
import matplotlib.patheffects as path_effects

def add_median_labels(ax):
    lines = ax.get_lines()
    # determine number of lines per box (this varies with/without fliers)
    boxes = [c for c in ax.get_children() if type(c).__name__ == 'PathPatch']
    lines_per_box = int(len(lines) / len(boxes))
    # iterate over median lines
    for median in lines[4:len(lines):lines_per_box]:
        # display median value at center of median line
        x, y = (data.mean() for data in median.get_data())
        # choose value depending on horizontal or vertical plot orientation
        value = x if (median.get_xdata()[1]-median.get_xdata()[0]) == 0 else y
        text = ax.text(x, y, f'{value:.1f}', ha='center', va='center',
                       fontweight='bold', color='white', bbox=dict(facecolor='black'),size=15)
        # create median-colored border around white text for contrast
        text.set_path_effects([
            path_effects.Stroke(linewidth=3, foreground=median.get_color()),
            path_effects.Normal(),
        ])
            
def box_and_histogram(column,  figsize=(10,10), bins = None):
    """ Boxplot and histogram together, with median labels on boxplot
    df_series: dataframe column
    figsize: size of fig (default (9,8))
    bins: number of bins (default None / auto)
    color of mean is green and median is black
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(nrows = 2, # Number of rows of the subplot grid= 2
                                           sharex = True, # x-axis will be shared among all subplots
                                           gridspec_kw = {"height_ratios": (.25, .75)}, 
                                           figsize = figsize 
                                            ) # creating the 2 subplots
    box_plot = sns.boxplot(column, ax=ax_box2,showmeans=True, color='red')
    add_median_labels(box_plot.axes)
 

    sns.distplot(column, kde=F, bins=bins) if bins else sns.distplot(column, kde=True, ) # For histogram
    ax_hist2.axvline(np.mean(column), color='g', linestyle='--') # Add mean to the histogram
    ax_hist2.axvline(np.median(column), color='black', linestyle='-') # Add median to the histogram

    
# function to create labeled barplots


def labeled_barplot(data, feature, perc=False, n=None):
    """
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)
    """

    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 1, 5))
    else:
        plt.figure(figsize=(n + 1, 5))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(
        data=data,
        x=feature,
        palette="RdYlBu",
        order=data[feature].value_counts().index
    
       
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage

    plt.show()  # show the plot


# let's explore discounts further
labeled_barplot(data, "GICS Sector", perc=True)


# Let's visualize the data for ['Current Price']
columns =  ['Current Price']
for col in columns:
    box_and_histogram(data[col])


# Let's visualize the data for ['Price Change']
columns =  ['Price Change']
for col in columns:
    box_and_histogram(data[col])


# Let's visualize the data for ['Volatility']
columns =  ['Volatility']
for col in columns:
    box_and_histogram(data[col])


# Let's visualize the data for ['ROE']
columns =  ['ROE']
for col in columns:
    box_and_histogram(data[col])


# Let's visualize the data for ['Cash Ratio']
columns =  ['Cash Ratio']
for col in columns:
    box_and_histogram(data[col])


# Let's visualize the data for ['Net Cash Flow']
columns =  ['Net Cash Flow']
for col in columns:
    box_and_histogram(data[col])


# Let's visualize the data for ['Net Income']
columns =  ['Net Income']
for col in columns:
    box_and_histogram(data[col])


# Let's visualize the data for ['Earnings Per Share']
columns =  ['Earnings Per Share']
for col in columns:
    box_and_histogram(data[col])


# Let's visualize the data for ['Estimated Shares Outstanding']
columns =  ['Estimated Shares Outstanding']
for col in columns:
    box_and_histogram(data[col])


# Let's visualize the data for ['P_E Ratio']
columns =  ['P_E Ratio']
for col in columns:
    box_and_histogram(data[col])


# Let's visualize the data for ['P_B Ratio']
columns =  ['P_B Ratio']
for col in columns:
    box_and_histogram(data[col])


# selecting numerical columns
num_col = data.select_dtypes(include=np.number).columns.tolist()
num_col

['Current Price',
 'Price Change',
 'Volatility',
 'ROE',
 'Cash Ratio',
 'Net Cash Flow',
 'Net Income',
 'Earnings Per Share',
 'Estimated Shares Outstanding',
 'P_E Ratio',
 'P_B Ratio']


# function to plot a boxplot and a histogram along the same scale.


def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (12,7))
    kde: whether to the show density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a star will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram


for item in num_col:
    histogram_boxplot(data, item)


sns.pairplot(data=data[num_col], diag_kind="kde")
plt.show()


fig, axes = plt.subplots(6, 2, figsize=(20, 15))
fig.suptitle("CDF plot of numerical variables", fontsize=20)
counter = 0
for ii in range(6):
    sns.ecdfplot(ax=axes[ii][0], x=data[num_col[counter]])
    counter = counter + 1
    if counter != 11:
        sns.ecdfplot(ax=axes[ii][1], x=data[num_col[counter]])
        counter = counter + 1
    else:
        pass

fig.tight_layout(pad=2.0)


columns =  ['Price Change']
for col in columns:
    box_and_histogram(data[col])


vol_mean = data.groupby('GICS Sector')['Volatility'].agg(['mean', 'count']).sort_values(by='mean',ascending=False)
vol_mean


vol_results =vol_mean.reset_index()
vol_results


sns.set(rc={'figure.figsize':(21,7)})
sns.catplot(x="GICS Sector", y="mean", kind="bar", data=vol_results, height=7, aspect=3);


plt.figure(figsize=(15, 7))
sns.heatmap(data[num_col].corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral")
plt.show()


cash_ratio_mean = data.groupby('GICS Sector')['Cash Ratio'].agg(['mean', 'count']).sort_values(by='mean',ascending=False)
cash_ratio_mean


cash_ratio_results = cash_ratio_mean.reset_index()
cash_ratio_results


sns.set(rc={'figure.figsize':(21,7)})
sns.catplot(x="GICS Sector", y="mean", kind="bar", data=cash_ratio_results, height=7, aspect=3);


P_E_mean = data.groupby('GICS Sector')['P_E Ratio'].agg(['mean', 'count']).sort_values(by='mean',ascending=False)
P_E_mean


P_E_results = P_E_mean.reset_index()
P_E_results


sns.set(rc={'figure.figsize':(21,7)})
sns.catplot(x="GICS Sector", y="mean", kind="bar", data=P_E_results, height=7, aspect=3);


# scaling the dataset before clustering
scaler = StandardScaler()
subset = data[num_col].copy()
subset_scaled = scaler.fit_transform(subset)


# creating a dataframe of the scaled columns
subset_scaled_df = pd.DataFrame(subset_scaled, columns=subset.columns)


for item in num_col:
    histogram_boxplot(subset_scaled_df, item)


clusters = range(1, 12)
meanDistortions = []

for k in clusters:
    model = KMeans(n_clusters=k)
    model.fit(subset_scaled_df)
    prediction = model.predict(subset_scaled_df)
    distortion = (
        sum(
            np.min(cdist(subset_scaled_df, model.cluster_centers_, "euclidean"), axis=1)
        )
        / subset_scaled_df.shape[0]
    )

    meanDistortions.append(distortion)

    print("Number of Clusters:", k, "\tAverage Distortion:", distortion)

plt.plot(clusters, meanDistortions, "bx-")
plt.xlabel("k")
plt.ylabel("Average Distortion")
plt.title("Selecting k with the Elbow Method", fontsize=20)

Number of Clusters: 1 	Average Distortion: 2.5425069919221697
Number of Clusters: 2 	Average Distortion: 2.382318498894466
Number of Clusters: 3 	Average Distortion: 2.2692367155390745
Number of Clusters: 4 	Average Distortion: 2.176396791566185
Number of Clusters: 5 	Average Distortion: 2.128799332840716
Number of Clusters: 6 	Average Distortion: 2.0591416288820374
Number of Clusters: 7 	Average Distortion: 1.9826333396712665
Number of Clusters: 8 	Average Distortion: 1.9753526418461937
Number of Clusters: 9 	Average Distortion: 1.8964970616244075
Number of Clusters: 10 	Average Distortion: 1.8539123989265462
Number of Clusters: 11 	Average Distortion: 1.7997730037404913

Text(0.5, 1.0, 'Selecting k with the Elbow Method')


sil_score = []
cluster_list = list(range(2, 15))
for n_clusters in cluster_list:
    clusterer = KMeans(n_clusters=n_clusters)
    preds = clusterer.fit_predict((subset_scaled_df))
    # centers = clusterer.cluster_centers_
    score = silhouette_score(subset_scaled_df, preds)
    sil_score.append(score)
    print("For n_clusters = {}, the silhouette score is {})".format(n_clusters, score))

plt.plot(cluster_list, sil_score)
plt.show()

For n_clusters = 2, the silhouette score is 0.43969639509980457)
For n_clusters = 3, the silhouette score is 0.45797710447228496)
For n_clusters = 4, the silhouette score is 0.45434371948348606)
For n_clusters = 5, the silhouette score is 0.40759857447931497)
For n_clusters = 6, the silhouette score is 0.4194206033258803)
For n_clusters = 7, the silhouette score is 0.4185436281362793)
For n_clusters = 8, the silhouette score is 0.4146191398667421)
For n_clusters = 9, the silhouette score is 0.38967567488717586)
For n_clusters = 10, the silhouette score is 0.13143171655836627)
For n_clusters = 11, the silhouette score is 0.12272712489085548)
For n_clusters = 12, the silhouette score is 0.15775934538491893)
For n_clusters = 13, the silhouette score is 0.16265801494654208)
For n_clusters = 14, the silhouette score is 0.16545323314088417)


# finding optimal no. of clusters with silhouette coefficients
visualizer = SilhouetteVisualizer(KMeans(6, random_state=1))
visualizer.fit(subset_scaled_df)
visualizer.show()

<AxesSubplot:title={'center':'Silhouette Plot of KMeans Clustering for 340 Samples in 6 Centers'}, xlabel='silhouette coefficient values', ylabel='cluster label'>


# finding optimal no. of clusters with silhouette coefficients
visualizer = SilhouetteVisualizer(KMeans(7, random_state=1))
visualizer.fit(subset_scaled_df)
visualizer.show()

<AxesSubplot:title={'center':'Silhouette Plot of KMeans Clustering for 340 Samples in 7 Centers'}, xlabel='silhouette coefficient values', ylabel='cluster label'>


# finding optimal no. of clusters with silhouette coefficients
visualizer = SilhouetteVisualizer(KMeans(8, random_state=1))
visualizer.fit(subset_scaled_df)
visualizer.show()

<AxesSubplot:title={'center':'Silhouette Plot of KMeans Clustering for 340 Samples in 8 Centers'}, xlabel='silhouette coefficient values', ylabel='cluster label'>


# finding optimal no. of clusters with silhouette coefficients
visualizer = SilhouetteVisualizer(KMeans(11, random_state=1))
visualizer.fit(subset_scaled_df)
visualizer.show()

<AxesSubplot:title={'center':'Silhouette Plot of KMeans Clustering for 340 Samples in 11 Centers'}, xlabel='silhouette coefficient values', ylabel='cluster label'>


# finding optimal no. of clusters with silhouette coefficients
visualizer = SilhouetteVisualizer(KMeans(12, random_state=1))
visualizer.fit(subset_scaled_df)
visualizer.show()

<AxesSubplot:title={'center':'Silhouette Plot of KMeans Clustering for 340 Samples in 12 Centers'}, xlabel='silhouette coefficient values', ylabel='cluster label'>


# finding optimal no. of clusters with silhouette coefficients
visualizer = SilhouetteVisualizer(KMeans(13, random_state=1))
visualizer.fit(subset_scaled_df)
visualizer.show()

<AxesSubplot:title={'center':'Silhouette Plot of KMeans Clustering for 340 Samples in 13 Centers'}, xlabel='silhouette coefficient values', ylabel='cluster label'>


# let's take 8 as number of clusters
kmeans = KMeans(n_clusters=8, random_state=0)
kmeans.fit(subset_scaled_df)

KMeans(random_state=0)


# adding kmeans cluster labels to the original and scaled dataframes

data["K_means_segments"] = kmeans.labels_
subset_scaled_df["K_means_segments"] = kmeans.labels_


cluster_profile = data.groupby("K_means_segments").mean()


cluster_profile["count_in_each_segments"] = (
    data.groupby("K_means_segments")["P_E Ratio"].count().values
)


# let's display cluster profiles
cluster_profile.style.highlight_max(color="lightgreen", axis=0)


plt.figure(figsize=(20, 40))
plt.suptitle("Boxplot of scaled numerical variables for each cluster", fontsize=20)
counter = 0
for  i, ii in enumerate(range(11)):
    plt.subplot(3, 5, i + 1)
    sns.boxplot(
        y=subset_scaled_df[num_col[counter]],
        x=subset_scaled_df["K_means_segments"],
    )
    counter = counter + 1
    plt.tight_layout()
    
plt.show()


plt.figure(figsize=(20, 40))
plt.suptitle("Boxplot of original numerical variables for each cluster", fontsize=20)
counter = 0
for  i, ii in enumerate(range(11)):
    plt.subplot(3, 5, i + 1)
    sns.boxplot(
        y=data[num_col[counter]],
        x=data["K_means_segments"],
    )
    counter = counter + 1
    plt.tight_layout()
    
plt.show()


# list of distance metrics
distance_metrics = ["euclidean", "chebyshev", "mahalanobis", "cityblock"]

# list of linkage methods
linkage_methods = ["single", "complete", "average", "weighted"]

high_cophenet_corr = 0
high_dm_lm = [0, 0]

for dm in distance_metrics:
    for lm in linkage_methods:
        Z = linkage(subset_scaled_df, metric=dm, method=lm)
        c, coph_dists = cophenet(Z, pdist(subset_scaled_df))
        print(
            "Cophenetic correlation for {} distance and {} linkage is {}.".format(
                dm.capitalize(), lm, c
            )
        )
        if high_cophenet_corr < c:
            high_cophenet_corr = c
            high_dm_lm[0] = dm
            high_dm_lm[1] = lm

Cophenetic correlation for Euclidean distance and single linkage is 0.9345526329379537.
Cophenetic correlation for Euclidean distance and complete linkage is 0.8452929808109975.
Cophenetic correlation for Euclidean distance and average linkage is 0.9495821453768725.
Cophenetic correlation for Euclidean distance and weighted linkage is 0.9156327901892896.
Cophenetic correlation for Chebyshev distance and single linkage is 0.9177074000650272.
Cophenetic correlation for Chebyshev distance and complete linkage is 0.8031917698414331.
Cophenetic correlation for Chebyshev distance and average linkage is 0.93724310450871.
Cophenetic correlation for Chebyshev distance and weighted linkage is 0.8906157116060892.
Cophenetic correlation for Mahalanobis distance and single linkage is 0.9306417886860311.
Cophenetic correlation for Mahalanobis distance and complete linkage is 0.7932196483047811.
Cophenetic correlation for Mahalanobis distance and average linkage is 0.9371656111212013.
Cophenetic correlation for Mahalanobis distance and weighted linkage is 0.8437991848610089.
Cophenetic correlation for Cityblock distance and single linkage is 0.9411400419314817.
Cophenetic correlation for Cityblock distance and complete linkage is 0.8020658406850402.
Cophenetic correlation for Cityblock distance and average linkage is 0.9338637157423139.
Cophenetic correlation for Cityblock distance and weighted linkage is 0.7239584217766201.


# printing the combination of distance metric and linkage method with the highest cophenetic correlation
print(
    "Highest cophenetic correlation is {}, which is obtained with {} distance and {} linkage.".format(
        high_cophenet_corr, high_dm_lm[0].capitalize(), high_dm_lm[1]
    )
)

Highest cophenetic correlation is 0.9495821453768725, which is obtained with Euclidean distance and average linkage.


# list of linkage methods
linkage_methods = ["single", "complete", "average", "centroid", "ward", "weighted"]

high_cophenet_corr = 0
high_dm_lm = [0, 0]

for lm in linkage_methods:
    Z = linkage(subset_scaled_df, metric="euclidean", method=lm)
    c, coph_dists = cophenet(Z, pdist(subset_scaled_df))
    print("Cophenetic correlation for {} linkage is {}.".format(lm, c))
    if high_cophenet_corr < c:
        high_cophenet_corr = c
        high_dm_lm[0] = "euclidean"
        high_dm_lm[1] = lm

Cophenetic correlation for single linkage is 0.9345526329379537.
Cophenetic correlation for complete linkage is 0.8452929808109975.
Cophenetic correlation for average linkage is 0.9495821453768725.
Cophenetic correlation for centroid linkage is 0.9471718133891771.
Cophenetic correlation for ward linkage is 0.7095968268387225.
Cophenetic correlation for weighted linkage is 0.9156327901892896.


# printing the combination of distance metric and linkage method with the highest cophenetic correlation
print(
    "Highest cophenetic correlation is {}, which is obtained with {} linkage.".format(
        high_cophenet_corr, high_dm_lm[1]
    )
)

Highest cophenetic correlation is 0.9495821453768725, which is obtained with average linkage.


# list of linkage methods
linkage_methods = ["single", "complete", "average", "centroid", "ward", "weighted"]

# lists to save results of cophenetic correlation calculation
compare_cols = ["Linkage", "Cophenetic Coefficient"]
compare = []

# to create a subplot image
fig, axs = plt.subplots(len(linkage_methods), 1, figsize=(15, 30))

# We will enumerate through the list of linkage methods above
# For each linkage method, we will plot the dendrogram and calculate the cophenetic correlation
for i, method in enumerate(linkage_methods):
    Z = linkage(subset_scaled_df, metric="euclidean", method=method)

    dendrogram(Z, ax=axs[i])
    axs[i].set_title(f"Dendrogram ({method.capitalize()} Linkage)")

    coph_corr, coph_dist = cophenet(Z, pdist(subset_scaled_df))
    axs[i].annotate(
        f"Cophenetic\nCorrelation\n{coph_corr:0.2f}",
        (0.80, 0.80),
        xycoords="axes fraction",
    )

    compare.append([method, coph_corr])


# let's create a dataframe to compare cophenetic correlations for each linkage method
df_cc = pd.DataFrame(compare, columns=compare_cols)
df_cc


# list of distance metrics
distance_metrics = ["mahalanobis", "cityblock"]

# list of linkage methods
linkage_methods = ["average","weighted"]

# to create a subplot image
fig, axs = plt.subplots(
    len(distance_metrics) + len(distance_metrics), 1, figsize=(10, 30)
)

i = 0
for dm in distance_metrics:
    for lm in linkage_methods:
        Z = linkage(subset_scaled_df, metric=dm, method=lm)

        dendrogram(Z, ax=axs[i])
        axs[i].set_title("Distance metric: {}\nLinkage: {}".format(dm.capitalize(), lm))

        coph_corr, coph_dist = cophenet(Z, pdist(subset_scaled_df))
        axs[i].annotate(
            f"Cophenetic\nCorrelation\n{coph_corr:0.2f}",
            (0.80, 0.80),
            xycoords="axes fraction",
        )
        i += 1


HCmodel = AgglomerativeClustering(n_clusters=10, affinity="euclidean", linkage="ward")
HCmodel.fit(subset_scaled_df)

AgglomerativeClustering(n_clusters=10)


# adding hierarchical cluster labels to the original and scaled dataframes

subset_scaled_df["HC_Clusters"] = HCmodel.labels_
data["HC_Clusters"] = HCmodel.labels_


cluster_profile = data.groupby("HC_Clusters").mean()


cluster_profile["count_in_each_segments"] = (
    data.groupby("HC_Clusters")["P_E Ratio"].count().values
)


# let's display cluster profiles
cluster_profile.style.highlight_max(color="lightgreen", axis=0)


HCmodel = AgglomerativeClustering(n_clusters=8, affinity="euclidean", linkage="ward")
HCmodel.fit(subset_scaled_df)

AgglomerativeClustering(n_clusters=8)


# adding hierarchical cluster labels to the original and scaled dataframes

subset_scaled_df["HC_Clusters"] = HCmodel.labels_
data["HC_Clusters"] = HCmodel.labels_


cluster_profile = data.groupby("HC_Clusters").mean()


cluster_profile["count_in_each_segments"] = (
    data.groupby("HC_Clusters")["P_E Ratio"].count().values
)


# let's display cluster profiles
cluster_profile.style.highlight_max(color="lightgreen", axis=0)


HCmodel = AgglomerativeClustering(n_clusters=7, affinity="euclidean", linkage="ward")
HCmodel.fit(subset_scaled_df)

AgglomerativeClustering(n_clusters=7)


# adding hierarchical cluster labels to the original and scaled dataframes

subset_scaled_df["HC_Clusters"] = HCmodel.labels_
data["HC_Clusters"] = HCmodel.labels_


cluster_profile = data.groupby("HC_Clusters").mean()


cluster_profile["count_in_each_segments"] = (
    data.groupby("HC_Clusters")["P_E Ratio"].count().values
)


# let's display cluster profiles
cluster_profile.style.highlight_max(color="lightgreen", axis=0)


plt.figure(figsize=(20, 40))
plt.suptitle("Boxplot of scaled numerical variables for each cluster", fontsize=20)
counter = 0
for  i, ii in enumerate(range(11)):
    plt.subplot(3, 5, i + 1)
    sns.boxplot(
        y=subset_scaled_df[num_col[counter]],
        x=subset_scaled_df["HC_Clusters"],
    )
    counter = counter + 1
    plt.tight_layout()


plt.figure(figsize=(20, 40))
plt.suptitle("Boxplot of original numerical variables for each cluster", fontsize=20)
counter = 0
for  i, ii in enumerate(range(11)):
    plt.subplot(3, 5, i + 1)
    sns.boxplot(
        y=data[num_col[counter]],
        x=data["HC_Clusters"],
    )
    counter = counter + 1
    plt.tight_layout()


# importing library
from sklearn.decomposition import PCA

# setting the number of components to 2
pca = PCA(n_components=2)

# transforming data and storing results in a dataframe
X_reduced_pca = pca.fit_transform(subset_scaled_df)
reduced_df_pca = pd.DataFrame(
    data=X_reduced_pca, columns=["Component 1", "Component 2"]
)


# checking the amount of variance explained
pca.explained_variance_ratio_.sum()

0.37836198332699145


sns.scatterplot(data=reduced_df_pca, x="Component 1", y="Component 2")

<AxesSubplot:xlabel='Component 1', ylabel='Component 2'>


sns.scatterplot(
    data=reduced_df_pca,
    x="Component 1",
    y="Component 2",
    hue=data["HC_Clusters"],
    palette="rainbow",
)
plt.legend(bbox_to_anchor=(1, 1))

<matplotlib.legend.Legend at 0x29e8c289340>

	Ticker Symbol	Security	GICS Sector	GICS Sub Industry	Current Price	Price Change	Volatility	ROE	Cash Ratio	Net Cash Flow	Net Income	Earnings Per Share	Estimated Shares Outstanding	P/E Ratio	P/B Ratio
102	DVN	Devon Energy Corp.	Energy	Oil & Gas Exploration & Production	32.000000	-15.478079	2.923698	205	70	830000000	-14454000000	-35.550	4.065823e+08	93.089287	1.785616
125	FB	Facebook	Information Technology	Internet Software & Services	104.660004	16.224320	1.320606	8	958	592000000	3669000000	1.310	2.800763e+09	79.893133	5.884467
11	AIV	Apartment Investment & Mgmt	Real Estate	REITs	40.029999	7.578608	1.163334	15	47	21818000	248710000	1.520	1.636250e+08	26.335526	-1.269332
248	PG	Procter & Gamble	Consumer Staples	Personal Products	79.410004	10.660538	0.806056	17	129	160383000	636056000	3.280	4.913916e+08	24.070121	-2.256747
238	OXY	Occidental Petroleum	Energy	Oil & Gas Exploration & Production	67.610001	0.865287	1.589520	32	64	-588000000	-7829000000	-10.230	7.652981e+08	93.089287	3.345102
336	YUM	Yum! Brands Inc	Consumer Discretionary	Restaurants	52.516175	-8.698917	1.478877	142	27	159000000	1293000000	2.970	4.353535e+08	17.682214	-3.838260
112	EQT	EQT Corporation	Energy	Oil & Gas Exploration & Production	52.130001	-21.253771	2.364883	2	201	523803000	85171000	0.560	1.520911e+08	93.089287	9.567952
147	HAL	Halliburton Co.	Energy	Oil & Gas Equipment & Services	34.040001	-5.101751	1.966062	4	189	7786000000	-671000000	-0.790	8.493671e+08	93.089287	17.345857
89	DFS	Discover Financial Services	Financials	Consumer Finance	53.619999	3.653584	1.159897	20	99	2288000000	2297000000	5.140	4.468872e+08	10.431906	-0.375934
173	IVZ	Invesco Ltd.	Financials	Asset Management & Custody Banks	33.480000	7.067477	1.580839	12	67	412000000	968100000	2.260	4.283628e+08	14.814159	4.218620
117	ETR	Entergy Corp.	Utilities	Electric Utilities	68.360001	4.910983	1.217401	2	44	-71065000	-156734000	-0.990	1.583172e+08	18.456543	6.174024
230	NSC	Norfolk Southern Corp.	Industrials	Railroads	84.589996	9.529966	2.168814	13	49	128000000	1556000000	5.130	3.033138e+08	16.489278	0.926433
123	F	Ford Motor	Consumer Discretionary	Automobile Manufacturers	14.090000	2.398256	1.151454	26	43	3515000000	7373000000	1.860	3.963978e+09	7.575269	5.108756
161	HST	Host Hotels & Resorts	Real Estate	REITs	15.340000	-3.217666	1.594628	8	47	-445000000	558000000	0.220	2.536364e+09	69.727273	-0.113548
4	ADI	Analog Devices, Inc.	Information Technology	Semiconductors	55.320000	-1.827858	1.701169	14	272	315120000	696878000	0.310	2.247994e+09	178.451613	1.059810
246	PFE	Pfizer Inc.	Health Care	Pharmaceuticals	32.279999	3.130991	1.238748	11	79	298000000	6960000000	1.130	6.159292e+09	28.566371	-4.213309
290	TGNA	Tegna, Inc.	Consumer Discretionary	Publishing	25.520000	13.624226	1.797269	21	21	10716000	459522000	2.040	2.252559e+08	12.509804	-12.726553
275	SNI	Scripps Networks Interactive Inc.	Consumer Discretionary	Broadcasting & Cable TV	55.209999	12.238260	1.773865	40	23	-654720000	606828000	4.680	1.296641e+08	11.797008	-7.961579
286	SYK	Stryker Corp.	Health Care	Health Care Equipment	92.940002	-1.650792	1.138163	17	116	1584000000	1439000000	3.820	3.767016e+08	24.329843	7.026782
219	MUR	Murphy Oil	Energy	Integrated Oil & Gas	22.450001	-8.591197	2.851180	43	27	-910125000	-2270833000	-13.030	1.742773e+08	28.407929	-1.298006
305	UNM	Unum Group	Financials	Diversified Financial Services	33.290001	3.804181	1.102848	10	117	10400000	867100000	3.510	2.470370e+08	9.484331	-4.178927
119	EXC	Exelon Corp.	Utilities	MultiUtilities	27.770000	-6.403775	1.351595	9	74	4624000000	2269000000	2.685	2.998871e+08	17.313076	-1.715880
251	PM	Philip Morris International	Consumer Staples	Tobacco	87.910004	10.328820	0.861453	52	22	1735000000	6873000000	4.420	1.554977e+09	19.889141	-1.418027
6	ADS	Alliance Data Systems	Information Technology	Data Processing & Outsourced Services	276.570007	6.189286	1.116976	30	25	90885000	596541000	8.910	6.695185e+07	31.040405	129.064585
337	ZBH	Zimmer Biomet Holdings	Health Care	Health Care Equipment	102.589996	9.347683	1.404206	1	100	376000000	147000000	0.780	1.884615e+08	131.525636	-23.884449
122	EXR	Extra Space Storage	Real Estate	Specialized REITs	88.209999	13.922251	1.186059	19	39	28136000	394950000	1.580	2.499684e+08	55.829113	-14.151445
288	TAP	Molson Coors Brewing Company	Consumer Staples	Brewers	93.919998	13.129368	1.217803	5	35	-150300000	359500000	1.940	1.853093e+08	48.412370	-25.385129
250	PHM	Pulte Homes Inc.	Consumer Discretionary	Homebuilding	17.820000	-5.564393	1.694751	10	25	-533785000	494090000	1.380	3.580362e+08	12.913043	-0.307832
315	VRTX	Vertex Pharmaceuticals Inc	Health Care	Biotechnology	125.830002	21.928300	2.456535	59	221	89509000	-556334000	-2.310	2.408372e+08	39.602928	2.559671
267	RRC	Range Resources Corp.	Energy	Oil & Gas Exploration & Production	24.610001	-25.106512	3.712995	26	0	23000	-713685000	-4.290	1.663601e+08	93.089287	0.525090
197	MAC	Macerich	Real Estate	Retail REITs	80.690002	4.183351	1.169328	10	47	1603000	487562000	3.080	1.582994e+08	26.198053	-3.973395
177	JPM	JPMorgan Chase & Co.	Financials	Banks	66.029999	8.033377	1.130337	10	99	-7341000000	24442000000	6.050	4.040000e+09	10.914049	-1.886881
228	NLSN	Nielsen Holdings	Industrials	Research & Consulting Services	46.599998	4.931317	1.198493	13	21	84000000	570000000	1.550	3.677419e+08	30.064515	-12.375526
107	EIX	Edison Int'l	Utilities	Electric Utilities	59.209999	-6.135071	0.927260	10	3	29000000	1117000000	3.130	3.568690e+08	18.916933	-6.369284
18	ALLE	Allegion	Industrials	Building Products	65.919998	13.753230	1.283795	601	45	-90800000	153900000	1.600	9.618750e+07	41.199999	-0.877453
314	VRSN	Verisign Inc.	Information Technology	Internet Software & Services	87.360001	23.459580	1.379480	35	127	37051000	375236000	3.290	1.140535e+08	26.553192	4.076543
59	CF	CF Industries Holdings Inc	Materials	Fertilizers & Agricultural Chemicals	40.810001	-9.250611	2.368186	16	25	-1710600000	664900000	2.970	2.238721e+08	13.740741	-0.393528
131	FLIR	FLIR Systems	Information Technology	Electronic Equipment & Instruments	28.070000	0.214209	1.761193	15	81	-58589000	241686000	1.730	1.397029e+08	16.225434	4.014713
234	O	Realty Income Corporation	Real Estate	Retail REITs	51.630001	8.420836	1.104581	4	47	36442000	283766000	1.090	2.603358e+08	47.366973	-3.973395
120	EXPD	Expeditors Int'l	Industrials	Air Freight & Logistics	45.099998	-4.449159	1.062553	27	94	-119311000	457223000	2.420	1.889351e+08	18.636363	5.991459
303	UHS	Universal Health Services, Inc.	Health Care	Health Care Facilities	119.489998	-5.136552	2.048697	16	6	29159000	680528000	6.890	9.877039e+07	17.342525	6.255903
159	HRL	Hormel Foods Corp.	Consumer Staples	Packaged Foods & Meats	39.540001	24.496225	1.078455	17	29	13065000	686088000	3.280	1.992378e+08	24.070121	-1.980483
158	HPQ	HP Inc.	Information Technology	Computer Hardware	11.840000	2.161759	2.373359	16	18	2300000000	4554000000	1.800	5.139877e+08	25.309524	3.954975
207	MKC	McCormick & Co.	Consumer Staples	Packaged Foods & Meats	85.559998	6.976738	1.032221	24	9	35300000	401600000	3.140	1.278981e+08	27.248407	-1.980483
321	WHR	Whirlpool Corp.	Consumer Discretionary	Household Appliances	146.869995	-0.230971	2.397803	17	10	-254000000	783000000	9.950	7.869347e+07	14.760804	-45.086335
95	DLPH	Delphi Automotive	Consumer Discretionary	Auto Parts & Equipment	85.730003	12.109326	1.440884	64	14	-325000000	1450000000	5.080	2.854331e+08	16.875985	-0.662152
80	CTSH	Cognizant Technology Solutions	Information Technology	IT Consulting & Other Services	60.020000	-4.654489	1.338123	17	182	115100000	1623600000	2.670	6.080899e+08	22.479401	7.121644
188	LLY	Lilly (Eli) & Co.	Health Care	Pharmaceuticals	84.260002	0.789478	1.440622	17	54	-205200000	2408400000	2.270	1.060969e+09	37.118944	-0.651103
332	XRAY	Dentsply Sirona	Health Care	Health Care Supplies	60.849998	19.901474	1.007230	11	60	133000000	251200000	1.790	1.403352e+08	33.994412	0.855096
92	DIS	The Walt Disney Company	Consumer Discretionary	Broadcasting & Cable TV	105.080002	2.049141	1.188454	19	26	848000000	8382000000	4.950	1.693333e+09	21.228283	-3.985039
268	RSG	Republic Services Inc	Industrials	Industrial Conglomerates	43.990002	6.745943	0.839821	10	2	-42800000	749900000	2.140	3.504206e+08	20.556076	-2.428225
65	CI	CIGNA Corp.	Health Care	Managed Health Care	146.330002	8.682415	1.588398	17	70	548000000	2094000000	8.170	2.563035e+08	17.910649	-8.805281
300	UAA	Under Armour	Consumer Discretionary	Apparel, Accessories & Luxury Goods	80.610001	-16.948277	1.758824	14	27	-463323000	232573000	3.030	2.135983e+08	20.819876	-0.857290
291	TMK	Torchmark Corp.	Financials	Life & Health Insurance	57.160000	1.168142	1.022968	13	99	-4636000	527100000	4.210	1.252019e+08	13.577197	-1.883912
12	AIZ	Assurant Inc	Financials	Multi-line Insurance	80.540001	1.897773	1.112604	3	99	-30351000	141555000	2.080	6.805529e+07	38.721154	-4.072615
201	MCD	McDonald's Corp.	Consumer Discretionary	Restaurants	118.139999	19.939085	0.733163	64	260	5607600000	4529300000	4.820	9.396888e+08	24.510373	7.122145
27	AN	AutoNation Inc	Consumer Discretionary	Specialty Stores	59.660000	2.350316	1.480914	19	1	-1300000	442600000	3.930	1.126209e+08	15.180662	-7.970104
233	NWL	Newell Brands	Consumer Discretionary	Housewares & Specialties	44.080002	9.980039	1.641300	19	14	75400000	350000000	1.300	2.692308e+08	33.907694	-2.075543
339	ZTS	Zoetis	Health Care	Pharmaceuticals	47.919998	16.678836	1.610285	32	65	272000000	339000000	0.680	4.985294e+08	70.470585	1.723068
211	MNST	Monster Beverage	Consumer Staples	Soft Drinks	49.653332	10.800357	1.585944	11	568	1805094000	546733000	3.710	1.469542e+09	25.420118	-5.190734
29	AON	Aon plc	Financials	Insurance Brokers	92.209999	3.910301	1.105032	23	99	10000000	1385000000	4.930	2.809331e+08	18.703854	-7.759856
91	DHR	Danaher Corp.	Industrials	Industrial Conglomerates	70.416985	8.924595	1.191466	14	13	-2214800000	3357400000	4.810	6.980042e+08	14.639706	-13.759230
236	OMC	Omnicom Group	Consumer Discretionary	Advertising	75.660004	14.810321	1.066369	45	18	217100000	1093900000	4.430	2.469300e+08	17.079008	-10.464098
93	DISCA	Discovery Communications-A	Consumer Discretionary	Cable & Satellite	26.680000	2.026769	1.689235	19	25	23000000	1034000000	-2.430	1.115226e+08	20.819876	-76.119077
58	CELG	Celgene Corp.	Health Care	Biotechnology	119.760002	8.448793	2.000828	27	333	758700000	1602000000	2.020	7.930693e+08	59.287130	-4.320051
132	FLR	Fluor Corp.	Industrials	Diversified Commercial Services	47.220001	10.819056	1.774454	14	73	-43239000	412512000	2.850	1.447411e+08	16.568421	14.992623
90	DGX	Quest Diagnostics	Health Care	Health Care Facilities	71.139999	15.674795	1.381490	15	11	-59000000	709000000	4.920	1.441057e+08	14.459349	-4.552214
320	WFC	Wells Fargo	Financials	Banks	54.360001	5.532912	0.969774	12	99	-460000000	22894000000	4.180	5.477033e+09	13.004785	-0.938007
312	VNO	Vornado Realty Trust	Real Estate	REITs	99.959999	10.027519	1.019724	11	47	637230000	760434000	3.610	2.106465e+08	27.689750	-1.081912
85	D	Dominion Resources	Utilities	Electric Utilities	67.639999	-3.988642	0.889931	15	8	289000000	1899000000	3.210	5.915888e+08	21.071651	-7.604945
73	CNC	Centene Corporation	Health Care	Managed Health Care	65.809998	21.712591	2.298696	16	70	150000000	355000000	2.990	1.187291e+08	22.010033	-1.305493
287	T	AT&T Inc	Telecommunications Services	Integrated Telecommunications Services	34.410000	5.942118	0.859442	11	11	-3482000000	13345000000	2.370	5.630802e+09	14.518987	-23.537323
304	UNH	United Health Group Inc.	Health Care	Managed Health Care	117.639999	1.466273	1.482349	17	70	3428000000	5813000000	6.100	9.529508e+08	19.285246	-8.805281
165	IDXX	IDEXX Laboratories	Health Care	Health Care Equipment	72.919998	-1.565880	1.469586	228	40	-193542000	192078000	2.070	9.279130e+07	35.227052	-0.981083
328	XEC	Cimarex Energy	Energy	Oil & Gas Exploration & Production	89.379997	-14.403372	2.397940	86	190	373520000	-2408948000	-25.920	9.293781e+07	93.089287	7.186128
167	INTC	Intel Corp.	Information Technology	Semiconductors	34.450001	14.035095	1.226022	19	162	12747000000	11420000000	2.410	4.738589e+09	14.294606	3.954975
14	AKAM	Akamai Technologies Inc	Information Technology	Internet Software & Services	52.630001	-23.790903	1.384502	10	225	50823000	321406000	1.800	1.785589e+08	29.238889	4.282358
244	PEG	Public Serv. Enterprise Inc.	Utilities	Electric Utilities	38.689999	-8.230553	1.180661	13	11	-8000000	1679000000	3.320	5.057229e+08	11.653614	-0.361858
105	ED	Consolidated Edison	Utilities	Electric Utilities	64.269997	-3.974306	1.068002	9	20	249000000	1193000000	4.070	2.931204e+08	15.791154	-3.022649
199	MAS	Masco Corp.	Industrials	Building Products	28.299999	11.637077	1.428359	263	61	85000000	355000000	1.030	3.446602e+08	27.475727	2.219577
51	BXP	Boston Properties	Real Estate	REITs	127.540001	7.203497	1.089469	10	47	-1039361000	583106000	3.790	1.538538e+08	33.651715	-1.269332
9	AFL	AFLAC Inc	Financials	Life & Health Insurance	59.900002	3.027181	1.048295	14	99	-308000000	2533000000	5.880	4.307823e+08	10.187075	-1.883912
127	FCX	Freeport-McMoran Cp & Gld	Materials	Copper	6.770000	-31.685167	3.796410	155	5	-240000000	-12156000000	-11.310	1.074801e+09	22.811951	2.935427
16	ALK	Alaska Air Group Inc	Industrials	Airlines	80.510002	2.066436	1.773431	35	74	-34000000	848000000	6.610	1.282905e+08	12.180031	-1.114658
0	AAL	American Airlines Group	Industrials	Airlines	42.349998	9.999995	1.687151	135	51	-604000000	7610000000	11.390	6.681299e+08	3.718174	-8.784219
284	SWN	Southwestern Energy	Energy	Oil & Gas Exploration & Production	7.110000	-44.798137	4.580042	200	2	-38000000	-4556000000	-6.070	4.021417e+08	93.089287	1.273530
272	SEE	Sealed Air	Materials	Paper Packaging	44.599998	-5.146750	1.580117	43	19	73000000	225400000	1.630	1.382822e+08	27.361962	-2.716908
62	CHK	Chesapeake Energy	Energy	Integrated Oil & Gas	4.500000	-38.101788	4.559815	687	22	-3283000000	-14685000000	-22.430	6.547035e+08	28.407929	-1.840528
185	LH	Laboratory Corp. of America Holding	Health Care	Health Care Facilities	123.639999	14.174899	1.603130	9	42	136400000	436900000	5.030	1.782465e+08	15.900937	-1.294844
169	IPG	Interpublic Group	Consumer Discretionary	Advertising	23.280001	21.821035	1.139799	23	20	-157700000	454600000	1.110	4.095495e+08	20.972974	0.265658
192	LUV	Southwest Airlines	Industrials	Airlines	43.060001	13.855106	1.536290	30	41	301000000	2181000000	3.300	6.609091e+08	13.048485	-5.117194
70	CMG	Chipotle Mexican Grill	Consumer Discretionary	Restaurants	479.850006	-33.131268	2.474002	22	237	-171460000	475602000	15.300	3.108510e+07	31.362745	17.201329
206	MJN	Mead Johnson	Consumer Staples	Packaged Foods & Meats	78.949997	12.081196	1.718403	103	136	403700000	653500000	3.280	1.992378e+08	24.070121	6.495755
111	EQR	Equity Residential	Real Estate	REITs	81.589996	8.037605	1.056186	8	47	2196000	870120000	2.370	3.671392e+08	34.426159	-1.269332
41	BAX	Baxter International Inc.	Health Care	Health Care Equipment	38.150002	16.702365	1.204526	11	128	-712000000	968000000	1.780	5.438202e+08	21.432585	8.637045
249	PGR	Progressive Corp.	Financials	Property & Casualty Insurance	31.799999	3.515625	1.086898	17	99	116000000	1267600000	2.160	5.868519e+08	14.722222	-0.843313
205	MHK	Mohawk Industries	Consumer Discretionary	Home Furnishings	189.389999	3.514425	1.492478	13	3	-16185000	615302000	2.590	2.375683e+08	73.123552	-3.980316
271	SE	Spectra Energy Corp.	Energy	Oil & Gas Refining & Marketing & Transportation	23.940001	-9.898378	2.030786	3	6	-2000000	196000000	0.290	6.758621e+08	82.551728	-2.580408
242	PCG	PG&E Corp.	Utilities	MultiUtilities	53.189999	0.510206	1.039803	5	6	-28000000	888000000	1.810	4.906077e+08	29.386740	-1.121059
338	ZION	Zions Bancorp	Financials	Regional Banks	27.299999	-1.158588	1.468176	4	99	-43623000	309471000	1.200	2.578925e+08	22.749999	-0.063096
39	BA	Boeing Company	Industrials	Aerospace & Defense	144.589996	10.105078	1.155905	82	24	-431000000	5176000000	7.520	6.882979e+08	19.227393	22.032612
106	EFX	Equifax Inc.	Industrials	Research & Consulting Services	111.370003	14.531063	1.081040	19	15	-35000000	429100000	3.610	1.188643e+08	30.850416	-8.116821
78	CSX	CSX Corp.	Industrials	Railroads	25.950001	-4.349421	1.626219	17	74	-41000000	1968000000	2.000	9.840000e+08	12.975001	0.902439
231	NTRS	Northern Trust Corp.	Financials	Asset Management & Custody Banks	72.089996	5.796884	1.281566	11	67	3394000000	973800000	4.030	2.416377e+08	17.888336	-13.398380
273	SHW	Sherwin-Williams	Materials	Specialty Chemicals	259.600006	16.537983	1.426488	121	10	165012000	1053849000	11.380	9.260536e+07	22.811951	2.825366
322	WM	Waste Management Inc.	Industrials	Environmental Services	53.369999	7.061186	0.940366	14	2	-1268000000	753000000	1.660	4.536145e+08	32.150602	-1.415299
17	ALL	Allstate Corp	Financials	Property & Casualty Insurance	62.090000	6.592275	1.053266	11	99	-162000000	2171000000	5.120	4.240234e+08	12.126953	-4.327138
256	PPL	PPL Corp.	Utilities	Electric Utilities	34.130001	3.424245	1.109059	7	22	-563000000	682000000	1.010	6.752475e+08	33.792080	-2.827111
223	NDAQ	NASDAQ OMX Group	Financials	Diversified Financial Services	58.169998	8.810324	1.563258	8	117	-126000000	428000000	2.560	1.671875e+08	22.722655	-11.717383
88	DE	Deere & Co.	Industrials	Construction & Farm Machinery & Heavy Trucks	76.269997	3.952561	1.551946	29	22	375200000	1940000000	4.030	5.322359e+08	14.842233	6.277287
292	TMO	Thermo Fisher Scientific	Health Care	Health Care Equipment	141.850006	15.607180	1.247751	9	11	-891400000	1975400000	4.960	3.982661e+08	28.598792	-28.032512
150	HCA	HCA Holdings	Health Care	Health Care Facilities	67.629997	-12.532337	1.914907	28	13	175000000	2129000000	5.140	4.142023e+08	13.157587	-7.279051
42	BBT	BB&T Corporation	Financials	Banks	37.810001	5.940045	1.077678	8	99	1386000000	2084000000	2.590	8.046332e+08	14.598456	-0.852562
180	KMI	Kinder Morgan	Energy	Oil & Gas Refining & Marketing & Transportation	14.920000	-47.129693	3.139352	1	7	-86000000	253000000	0.100	2.530000e+09	149.200000	-1.894071
171	ISRG	Intuitive Surgical Inc.	Health Care	Health Care Equipment	546.159973	18.733013	1.126009	14	317	114300000	588800000	15.870	3.710145e+07	34.414617	42.607500
138	GD	General Dynamics	Industrials	Aerospace & Defense	137.360001	-0.463767	0.939544	28	22	-1603000000	2965000000	9.230	3.212351e+08	14.881907	4.242998
162	HSY	The Hershey Company	Consumer Staples	Packaged Foods & Meats	89.269997	-3.261814	1.188383	51	16	-28325000	512951000	3.280	1.992378e+08	24.070121	-1.980483
191	LUK	Leucadia National Corp.	Financials	Multi-Sector Holdings	17.389999	-14.292764	1.554235	2	81	-638127000	252111000	0.740	3.406905e+08	23.499999	19.821416
5	ADM	Archer-Daniels-Midland Co	Consumer Staples	Agricultural Products	36.680000	-12.017268	1.516493	10	49	-189000000	1849000000	2.990	6.183946e+08	12.267559	7.496831
224	NEE	NextEra Energy	Utilities	MultiUtilities	103.889999	6.237855	1.023375	12	6	-6000000	2752000000	6.110	4.504092e+08	17.003273	-7.353314
174	JBHT	J. B. Hunt Transport Services	Industrials	Trucking	73.360001	2.961405	1.218373	33	1	-395000	427235000	3.690	1.157818e+08	19.880759	2.823845
38	AXP	American Express Co	Financials	Consumer Finance	69.550003	-6.216290	0.900066	25	99	474000000	5163000000	3.900	5.066604e+08	10.263506	-0.609074
232	NUE	Nucor Corp.	Materials	Steel	40.299999	6.585554	1.460619	5	147	915325000	357659000	1.110	3.222153e+08	36.306305	11.168107
179	KMB	Kimberly-Clark	Consumer Staples	Household Products	127.300003	17.511309	0.870405	582	10	-170000000	1013000000	2.780	3.643885e+08	45.791368	-2.533011
163	HUM	Humana Inc.	Health Care	Managed Health Care	178.509995	-0.145443	1.615206	12	70	636000000	1276000000	8.540	1.494145e+08	20.902810	-8.805281
139	GGP	General Growth Properties Inc.	Real Estate	Retail REITs	27.209999	4.212937	1.390342	17	47	-15576000	1374561000	3.040	1.582994e+08	47.366973	-3.973395
67	CL	Colgate-Palmolive	Consumer Staples	Household Products	66.620003	4.781379	0.895471	463	27	-119000000	1384000000	1.530	9.045752e+08	43.542486	-0.548324
187	LLL	L-3 Communications Holdings	Industrials	Industrial Conglomerates	119.510002	14.539013	1.513434	6	7	-235000000	-240000000	-2.970	8.080808e+07	17.334711	14.280750
146	GWW	Grainger (W.W.) Inc.	Industrials	Industrial Materials	202.589996	-5.336199	1.348597	34	16	63492000	768996000	11.690	6.578238e+07	17.330196	12.112879
100	DUK	Duke Energy	Utilities	Electric Utilities	71.389999	-0.833448	1.096727	7	8	-1179000000	2816000000	4.050	6.953086e+08	17.627160	-4.426811
34	ATVI	Activision Blizzard	Information Technology	Home Entertainment Software	38.709999	23.319529	1.886335	11	70	-3025000000	892000000	1.210	7.371901e+08	31.991735	0.290291
110	EQIX	Equinix	Real Estate	REITs	302.399994	10.019650	1.308082	7	164	1617921000	187774000	3.250	5.777662e+07	93.046152	23.856728
221	NAVI	Navient	Financials	Consumer Finance	11.450000	1.868327	2.230827	25	99	151000000	997000000	2.660	3.748120e+08	4.304511	-1.880943
319	WEC	Wec Energy Group Inc	Utilities	Electric Utilities	51.310001	-1.986623	1.103033	7	2	-12100000	640300000	2.360	2.713136e+08	21.741526	-1.850995
19	ALXN	Alexion Pharmaceuticals	Health Care	Biotechnology	190.750000	22.338380	2.022921	2	195	66000000	144000000	0.680	2.117647e+08	280.514706	-14.171389
212	MO	Altria Group Inc	Consumer Staples	Tobacco	58.209999	6.885788	0.959008	182	33	-952000000	5241000000	2.670	1.962921e+09	21.801498	-6.632971
44	BHI	Baker Hughes Inc	Energy	Oil & Gas Equipment & Services	46.150002	-12.312367	2.559553	12	84	584000000	-1967000000	-4.490	4.380846e+08	93.089287	13.490544
208	MLM	Martin Marietta Materials	Materials	Construction Materials	136.580002	-10.866015	2.164150	7	46	59758000	288792000	4.310	6.700510e+07	31.689096	3.050887
175	JEC	Jacobs Engineering Group	Industrials	Industrial Conglomerates	41.950001	11.539484	1.732990	7	23	-271788000	302971000	2.420	1.251946e+08	17.334711	6.294943
186	LKQ	LKQ Corporation	Consumer Discretionary	Distributors	29.629999	4.441304	1.427237	14	12	-27208000	423223000	3.030	2.135983e+08	20.819876	-0.857290
108	EMN	Eastman Chemical	Materials	Diversified Chemicals	67.510002	3.654238	1.404508	22	14	79000000	848000000	5.710	1.485114e+08	11.823118	-12.308821
296	TSN	Tyson Foods	Consumer Staples	Packaged Foods & Meats	53.330002	23.249369	1.586719	13	19	250000000	1220000000	3.280	1.992378e+08	24.070121	-1.980483
79	CTL	CenturyLink Inc	Telecommunications Services	Integrated Telecommunications Services	25.160000	0.159232	1.522194	6	3	-2000000	878000000	1.580	5.556962e+08	15.924051	-13.383212
323	WMB	Williams Cos.	Energy	Oil & Gas Exploration & Production	25.700001	-30.988186	3.719560	9	4	-140000000	-571000000	-0.760	7.513158e+08	93.089287	-14.561121
260	PX	Praxair Inc.	Materials	Industrial Gases	102.400002	0.293834	1.131240	35	8	21000000	1547000000	5.390	2.870130e+08	18.998145	0.574887
8	AEP	American Electric Power	Utilities	Electric Utilities	58.270000	2.371753	1.068485	11	9	13900000	2052300000	3.130	4.218978e+08	18.456543	-3.022649
154	HIG	Hartford Financial Svc.Gp.	Financials	Property & Casualty Insurance	43.459999	-5.005467	1.147332	10	99	49000000	1682000000	4.050	4.153086e+08	10.730864	-4.327138
99	DPS	Dr Pepper Snapple Group	Consumer Staples	Soft Drinks	93.199997	18.049399	1.150797	35	58	683000000	764000000	4.000	1.910000e+08	23.299999	-12.717277
299	TXN	Texas Instruments	Information Technology	Semiconductors	54.810001	9.971912	1.263479	30	126	-199000000	2986000000	2.860	1.044056e+09	19.164336	2.768051
329	XEL	Xcel Energy Inc	Utilities	MultiUtilities	35.910000	1.383405	1.015052	9	2	5332000	984485000	1.940	5.074665e+08	18.510309	-2.261927
274	SLG	SL Green Realty	Real Estate	Office REITs	112.980003	4.004424	1.091967	4	47	-26010000	284084000	1.020	2.785137e+08	110.764709	-3.089477
28	ANTM	Anthem Inc.	Health Care	Managed Health Care	139.440002	-0.620053	1.511654	11	70	-38200000	2560000000	9.730	2.631038e+08	14.330935	-31.006773
31	APC	Anadarko Petroleum Corp	Energy	Oil & Gas Exploration & Production	48.580002	-20.802083	2.435165	52	22	-6430000000	-6692000000	-13.180	5.077390e+08	93.089287	-12.860938
55	CBG	CBRE Group	Real Estate	Real Estate Services	34.580002	8.197757	1.297857	20	12	-200481000	547132000	1.640	3.336171e+08	21.085367	-3.415302
32	APH	Amphenol Corp	Information Technology	Electronic Components	52.230000	2.693667	1.007762	24	175	768300000	763500000	2.470	3.091093e+08	21.145749	8.202923
213	MOS	The Mosaic Company	Materials	Fertilizers & Agricultural Chemicals	27.590000	-11.229086	2.830675	10	62	-1098300000	1000400000	2.790	3.585663e+08	9.888889	5.846617
48	BMY	Bristol-Myers Squibb	Health Care	Health Care Distributors	68.790001	16.081680	1.498872	11	53	-3186000000	1565000000	0.940	1.664894e+09	73.180852	0.588026
283	SWKS	Skyworks Solutions	Information Technology	Semiconductors	76.830002	-8.513933	2.017394	25	225	237800000	798300000	4.210	1.896200e+08	18.249407	7.413777
293	TRIP	TripAdvisor	Consumer Discretionary	Internet & Direct Marketing Retail	85.250000	34.803917	1.578344	14	212	159000000	198000000	1.380	1.434783e+08	61.775362	2.627576
33	ARNC	Arconic Inc	Industrials	Aerospace & Defense	7.398807	1.647784	2.592065	3	37	42000000	-322000000	-0.310	1.038710e+09	18.687607	2.639814
35	AVB	AvalonBay Communities, Inc.	Real Estate	Residential REITs	184.130005	4.857630	1.132875	8	47	-108953000	741733000	5.540	1.338868e+08	33.236463	-3.089477
269	SCG	SCANA Corp	Utilities	MultiUtilities	60.490002	7.232764	1.266240	14	9	39000000	746000000	5.220	1.429119e+08	11.588123	-4.016461
63	CHRW	C. H. Robinson Worldwide	Industrials	Air Freight & Logistics	62.020000	-9.008221	1.185473	44	12	39289000	509699000	3.520	1.448009e+08	17.619318	1.117804
157	HPE	Hewlett Packard Enterprise	Information Technology	Technology Hardware, Storage & Peripherals	15.200000	-17.837838	3.400491	7	45	7523000000	2461000000	1.800	5.139877e+08	25.309524	3.954975
128	FE	FirstEnergy Corp	Utilities	Electric Utilities	31.730000	1.179844	1.238785	5	2	46000000	578000000	1.370	4.218978e+08	23.160584	-6.072561
46	BK	The Bank of New York Mellon Corp.	Financials	Banks	41.220001	5.422003	1.201660	8	99	-433000000	3158000000	2.730	1.156777e+09	15.098901	-3.321298
66	CINF	Cincinnati Financial	Financials	Property & Casualty Insurance	59.169998	9.777358	0.935812	10	99	-47000000	634000000	3.870	1.638243e+08	15.289405	-4.327138
189	LMT	Lockheed Martin Corp.	Industrials	Aerospace & Defense	217.149994	5.254227	0.903098	116	8	-356000000	3605000000	11.620	3.102410e+08	18.687607	-10.852854
172	ITW	Illinois Tool Works	Industrials	Industrial Machinery	92.680000	12.776831	1.142869	36	130	-900000000	1899000000	5.160	3.680233e+08	17.961240	7.586477
324	WU	Western Union Co	Information Technology	Internet Software & Services	17.910000	-2.610109	1.273051	60	16	-467300000	837800000	1.630	5.139877e+08	10.987730	-8.043772
333	XRX	Xerox Corp.	Information Technology	IT Consulting & Other Services	10.630000	9.474768	1.866680	5	26	-43000000	474000000	0.420	1.128571e+09	25.309524	-0.295949
327	WYNN	Wynn Resorts Ltd	Consumer Discretionary	Casinos & Gaming	69.190002	29.496541	3.794783	174	198	-102075000	195290000	1.930	1.011865e+08	35.849742	12.695712
325	WY	Weyerhaeuser Corp.	Real Estate	REITs	29.980000	8.544529	1.338067	10	116	-568000000	506000000	0.890	5.685393e+08	33.685393	2.284802
168	IP	International Paper	Materials	Paper Packaging	37.700001	-0.026513	1.301630	24	27	-831000000	938000000	2.250	4.168889e+08	16.755556	6.123934
47	BLL	Ball Corp	Materials	Metal & Glass Containers	72.730003	16.535816	1.386684	22	10	32600000	280900000	2.050	1.370244e+08	35.478050	-3.895657
113	ES	Eversource Energy	Utilities	MultiUtilities	51.070000	0.709921	1.232829	8	1	-14756000	878485000	2.770	3.171426e+08	18.436823	-1.169833
261	PYPL	PayPal	Information Technology	Data Processing & Outsourced Services	36.200001	17.456201	1.925754	9	25	-808000000	1228000000	1.000	1.228000e+09	36.200001	5.434039
40	BAC	Bank of America Corp	Financials	Banks	16.830000	8.440722	1.418688	6	99	20764000000	15888000000	4.180	8.450695e+08	13.004785	-0.938007
21	AME	AMETEK Inc	Industrials	Electrical Components & Equipment	53.590000	2.212474	1.089266	18	37	3390000	590859000	2.460	2.401866e+08	21.784553	-4.490342
101	DVA	DaVita Inc.	Health Care	Health Care Facilities	69.709999	-3.622291	1.211643	6	79	533875000	269732000	1.270	2.123874e+08	54.889763	1.962527
164	IBM	International Business Machines	Information Technology	IT Consulting & Other Services	137.619995	-5.292136	1.082881	92	24	-790000000	13190000000	13.480	9.784866e+08	10.209198	4.852391
69	CME	CME Group Inc.	Financials	Financial Exchanges & Data	90.599998	-2.402245	1.323348	6	99	326500000	1247000000	3.710	3.361186e+08	24.420485	-58.649536
53	CAT	Caterpillar Inc.	Industrials	Construction & Farm Machinery & Heavy Trucks	67.959999	3.550209	1.493553	17	25	-881000000	2512000000	3.540	7.096045e+08	19.197740	6.264053
137	FTR	Frontier Communications	Telecommunications Services	Integrated Telecommunications Services	4.670000	-2.301255	2.026818	3	496	254000000	-196000000	-0.290	6.758621e+08	14.518987	10.497704
24	AMP	Ameriprise Financial	Financials	Asset Management & Custody Banks	106.419998	-2.420686	1.222260	22	67	-281000000	1562000000	8.600	1.816279e+08	12.374418	-13.398380
278	SPGI	S&P Global, Inc.	Financials	Diversified Financial Services	98.580002	14.044424	1.080858	596	51	-1016000000	1156000000	4.260	2.713615e+08	23.140846	-4.178927
184	LEN	Lennar Corp.	Consumer Discretionary	Homebuilding	48.910000	1.705136	1.569167	14	25	-123369000	802894000	3.870	2.074661e+08	12.638243	-0.307832
134	FMC	FMC Corporation	Materials	Diversified Chemicals	39.130001	15.088238	2.175738	26	5	-30900000	489000000	3.660	1.336066e+08	10.691257	5.101546
258	PSX	Phillips 66	Energy	Oil & Gas Refining & Marketing & Transportation	81.800003	5.371643	1.379589	18	41	-2133000000	4227000000	7.780	5.433162e+08	10.514139	7.029056
116	ETN	Eaton Corporation	Industrials	Industrial Conglomerates	52.040001	1.166411	1.521430	13	10	-513000000	1979000000	4.250	4.656471e+08	12.244706	-8.639591
204	MET	MetLife Inc.	Financials	Life & Health Insurance	48.209999	1.366690	1.138650	8	99	1944000000	5310000000	4.610	1.151844e+09	10.457700	-1.883912
142	GM	General Motors	Consumer Discretionary	Automobile Manufacturers	34.009998	12.281271	1.344514	24	33	-3857000000	9687000000	6.110	1.585434e+09	5.566284	-4.892037
294	TRV	The Travelers Companies Inc.	Financials	Property & Casualty Insurance	112.860001	13.029548	0.959365	15	99	6000000	3439000000	10.990	3.129208e+08	10.269336	-0.891599
217	MTB	M&T Bank Corp.	Financials	Banks	121.180000	-0.361785	1.380390	7	99	-5317000	1079667000	7.220	1.495384e+08	16.783934	-0.938007
263	RCL	Royal Caribbean Cruises Ltd	Consumer Discretionary	Hotels, Resorts & Cruise Lines	101.209999	13.425973	1.556512	8	9	-67676000	665783000	3.030	2.197304e+08	33.402640	-15.727481
56	CCI	Crown Castle International Corp.	Real Estate	REITs	86.449997	9.569068	0.960191	21	36	3190000	1520992000	4.440	3.425658e+08	19.470720	-10.666679
61	CHD	Church & Dwight	Consumer Staples	Household Products	42.439999	1.047615	0.929026	20	38	-93000000	410400000	3.130	1.311182e+08	13.559105	-9.428134
270	SCHW	Charles Schwab Corporation	Financials	Investment Banking & Brokerage	32.930000	15.462833	1.456940	11	99	615000000	1447000000	1.040	1.391346e+09	31.663462	-0.130090
225	NEM	Newmont Mining Corp. (Hldg. Co.)	Materials	Gold	17.990000	10.844116	2.536050	2	198	379000000	220000000	0.430	5.116279e+08	41.837209	6.971864
222	NBL	Noble Energy Inc	Energy	Oil & Gas Exploration & Production	32.930000	7.298791	2.509437	24	57	-155000000	-2441000000	-6.070	4.021417e+08	93.089287	1.171229

	GICS Sector	GICS Sub Industry	0
0	Energy	Oil & Gas Exploration & Production	16
1	Real Estate	REITs	14
2	Industrials	Industrial Conglomerates	14
3	Information Technology	Internet Software & Services	12
4	Utilities	Electric Utilities	12
5	Health Care	Health Care Equipment	11
6	Utilities	MultiUtilities	11
7	Financials	Banks	10
8	Financials	Property & Casualty Insurance	8
9	Financials	Diversified Financial Services	7
10	Health Care	Biotechnology	7
11	Energy	Oil & Gas Refining & Marketing & Transportation	6
12	Consumer Staples	Packaged Foods & Meats	6
13	Information Technology	Semiconductors	6
14	Health Care	Pharmaceuticals	6
15	Health Care	Managed Health Care	5
16	Industrials	Airlines	5
17	Financials	Consumer Finance	5
18	Materials	Diversified Chemicals	5
19	Energy	Integrated Oil & Gas	5
20	Health Care	Health Care Facilities	5
21	Industrials	Industrial Machinery	5
22	Real Estate	Residential REITs	4
23	Financials	Asset Management & Custody Banks	4
24	Materials	Specialty Chemicals	4
25	Industrials	Research & Consulting Services	4
26	Consumer Discretionary	Hotels, Resorts & Cruise Lines	4
27	Telecommunications Services	Integrated Telecommunications Services	4
28	Consumer Staples	Soft Drinks	4
29	Industrials	Building Products	4
30	Industrials	Aerospace & Defense	4
31	Real Estate	Retail REITs	4
32	Industrials	Railroads	4
33	Consumer Discretionary	Internet & Direct Marketing Retail	4
34	Consumer Discretionary	Specialty Stores	3
35	Consumer Discretionary	Cable & Satellite	3
36	Financials	Regional Banks	3
37	Information Technology	IT Consulting & Other Services	3
38	Health Care	Health Care Distributors	3
39	Financials	Life & Health Insurance	3
40	Real Estate	Specialized REITs	3
41	Financials	Insurance Brokers	3
42	Industrials	Air Freight & Logistics	3
43	Consumer Discretionary	Restaurants	3
44	Energy	Oil & Gas Equipment & Services	3
45	Industrials	Construction & Farm Machinery & Heavy Trucks	3
46	Consumer Staples	Household Products	3
47	Health Care	Health Care Supplies	2
48	Information Technology	Application Software	2
49	Materials	Construction Materials	2
50	Information Technology	Data Processing & Outsourced Services	2
51	Consumer Discretionary	Advertising	2
52	Information Technology	Electronic Components	2
53	Consumer Discretionary	Auto Parts & Equipment	2
54	Financials	Investment Banking & Brokerage	2
55	Consumer Discretionary	Automobile Manufacturers	2
56	Consumer Staples	Tobacco	2
57	Consumer Discretionary	Broadcasting & Cable TV	2
58	Materials	Paper Packaging	2
59	Consumer Discretionary	Homebuilding	2
60	Materials	Fertilizers & Agricultural Chemicals	2
61	Consumer Discretionary	Leisure Products	2
62	Information Technology	Networking Equipment	1
63	Information Technology	Home Entertainment Software	1
64	Telecommunications Services	Alternative Carriers	1
65	Materials	Copper	1
66	Real Estate	Real Estate Services	1
67	Real Estate	Office REITs	1
68	Materials	Steel	1
69	Information Technology	Technology Hardware, Storage & Peripherals	1
70	Materials	Metal & Glass Containers	1
71	Information Technology	Electronic Equipment & Instruments	1
72	Materials	Industrial Gases	1
73	Materials	Gold	1
74	Information Technology	Semiconductor Equipment	1
75	Health Care	Life Sciences Tools & Services	1
76	Information Technology	Computer Hardware	1
77	Industrials	Trucking	1
78	Consumer Discretionary	Casinos & Gaming	1
79	Consumer Discretionary	Consumer Electronics	1
80	Consumer Discretionary	Distributors	1
81	Consumer Discretionary	Home Furnishings	1
82	Consumer Discretionary	Household Appliances	1
83	Consumer Discretionary	Housewares & Specialties	1
84	Consumer Discretionary	Motorcycle Manufacturers	1
85	Consumer Discretionary	Publishing	1
86	Consumer Discretionary	Specialty Retail	1
87	Consumer Discretionary	Tires & Rubber	1
88	Consumer Staples	Agricultural Products	1
89	Consumer Staples	Brewers	1
90	Consumer Staples	Drug Retail	1
91	Consumer Staples	Personal Products	1
92	Financials	Financial Exchanges & Data	1
93	Financials	Multi-Sector Holdings	1
94	Financials	Multi-line Insurance	1
95	Financials	Thrifts & Mortgage Finance	1
96	Consumer Discretionary	Apparel, Accessories & Luxury Goods	1
97	Industrials	Diversified Commercial Services	1
98	Industrials	Electrical Components & Equipment	1
99	Industrials	Environmental Services	1
100	Industrials	Human Resource & Employment Services	1
101	Industrials	Industrial Materials	1
102	Industrials	Technology, Hardware, Software and Supplies	1
103	Utilities	Water Utilities	1

	count	mean	std	min	25%	50%	75%	max
Current Price	340.0	8.086234e+01	9.805509e+01	4.500000e+00	3.855500e+01	5.970500e+01	9.288000e+01	1.274950e+03
Price Change	340.0	4.078194e+00	1.200634e+01	-4.712969e+01	-9.394838e-01	4.819505e+00	1.069549e+01	5.505168e+01
Volatility	340.0	1.525976e+00	5.917984e-01	7.331632e-01	1.134878e+00	1.385593e+00	1.695549e+00	4.580042e+00
ROE	340.0	3.959706e+01	9.654754e+01	1.000000e+00	9.750000e+00	1.500000e+01	2.700000e+01	9.170000e+02
Cash Ratio	340.0	7.002353e+01	9.042133e+01	0.000000e+00	1.800000e+01	4.700000e+01	9.900000e+01	9.580000e+02
Net Cash Flow	340.0	5.553762e+07	1.946365e+09	-1.120800e+10	-1.939065e+08	2.098000e+06	1.698108e+08	2.076400e+10
Net Income	340.0	1.494385e+09	3.940150e+09	-2.352800e+10	3.523012e+08	7.073360e+08	1.899000e+09	2.444200e+10
Earnings Per Share	340.0	2.776662e+00	6.587779e+00	-6.120000e+01	1.557500e+00	2.895000e+00	4.620000e+00	5.009000e+01
Estimated Shares Outstanding	340.0	5.770283e+08	8.458496e+08	2.767216e+07	1.588482e+08	3.096751e+08	5.731175e+08	6.159292e+09
P_E Ratio	340.0	3.261256e+01	4.434873e+01	2.935451e+00	1.504465e+01	2.081988e+01	3.176476e+01	5.280391e+02
P_B Ratio	340.0	-1.718249e+00	1.396691e+01	-7.611908e+01	-4.352056e+00	-1.067170e+00	3.917066e+00	1.290646e+02

	mean	count
GICS Sector
Energy	2.568777	30
Materials	1.816726	20
Information Technology	1.659801	33
Consumer Discretionary	1.595478	40
Health Care	1.541023	40
Industrials	1.416989	53
Telecommunications Services	1.341612	5
Financials	1.267255	49
Real Estate	1.206053	27
Consumer Staples	1.152675	19
Utilities	1.118018	24

	GICS Sector	mean	count
0	Energy	2.568777	30
1	Materials	1.816726	20
2	Information Technology	1.659801	33
3	Consumer Discretionary	1.595478	40
4	Health Care	1.541023	40
5	Industrials	1.416989	53
6	Telecommunications Services	1.341612	5
7	Financials	1.267255	49
8	Real Estate	1.206053	27
9	Consumer Staples	1.152675	19
10	Utilities	1.118018	24

Trade&Ahead¶

Context¶

Objective¶

Data Description¶

Importing necessary libraries and data¶

Check for any missing data¶

Let's check the duplicate data.¶

Let us check the data type of the columns¶

Lets check the different unique values for the object columns¶

Exploratory Data Analysis (EDA)¶

Univariate Analysis¶

Functions for plotting¶

Bivariate Analysis¶

Data Preprocessing¶

EDA¶

K-means Clustering¶

Let's check the silhouette scores.¶

Let's take 8 as the appropriate no. of clusters as the silhouette score is high and visual representation of the clusters at 8 gives an even distribution.¶

Cluster Profiling¶

Insights¶

Hierarchical Clustering¶

Let's explore different linkage methods with Euclidean distance only.¶

Let's see the dendrogram for Mahalanobis and Manhattan distances with average and weighted linkage methods (as they gave high cophenetic correlation values).¶

Cluster Profiling¶

Let's create 10 clusters.¶

Let's create 8 clusters.¶

Let's create 7 clusters.¶

Insights¶

K-means vs Hierarchical Clustering¶

Actionable Insights and Recommendations¶

Dimensionality Reduction using PCA for visualization¶

	count	unique	top	freq
Ticker Symbol	340	340	UNM	1
Security	340	340	EOG Resources	1
GICS Sector	340	11	Industrials	53
GICS Sub Industry	340	104	Oil & Gas Exploration & Production	16

	mean	count
GICS Sector
Information Technology	149.818182	33
Telecommunications Services	117.000000	5
Health Care	103.775000	40
Financials	98.591837	49
Consumer Staples	70.947368	19
Energy	51.133333	30
Real Estate	50.111111	27
Consumer Discretionary	49.575000	40
Materials	41.700000	20
Industrials	36.188679	53
Utilities	13.625000	24

	mean	count
GICS Sector
Energy	72.897709	30
Information Technology	43.782546	33
Real Estate	43.065585	27
Health Care	41.135272	40
Consumer Discretionary	35.211613	40
Consumer Staples	25.521195	19
Materials	24.585352	20
Utilities	18.719412	24
Industrials	18.259380	53
Financials	16.023151	49
Telecommunications Services	12.222578	5

	Current Price	Price Change	Volatility	ROE	Cash Ratio	Net Cash Flow	Net Income	Earnings Per Share	Estimated Shares Outstanding	P_E Ratio	P_B Ratio	count_in_each_segments
K_means_segments
0	508.534992	5.732177	1.504640	27.250000	150.875000	37895875.000000	1116994125.000000	15.965000	75654420.935000	43.727459	29.581664	8
1	80.152167	14.571437	1.829679	28.100000	321.850000	625929050.000000	942050500.000000	2.010500	790456335.265000	45.067457	8.307945	20
2	71.419603	4.948894	1.373081	25.337121	51.272727	19976181.818182	1588185079.545455	3.719489	438021401.544886	23.214179	-3.335092	264
3	34.231808	-15.515565	2.832069	48.037037	47.740741	-128651518.518519	-2444318518.518518	-6.284444	503031539.057037	75.627265	1.655990	27
4	46.672222	5.166566	1.079367	25.000000	58.333333	-3040666666.666667	14848444444.444445	3.435556	4564959946.222222	15.596051	-6.354193	9
5	84.355716	3.854981	1.827670	633.571429	33.571429	-568400000.000000	-4968157142.857142	-10.841429	398169036.442857	42.284541	-11.589502	7
6	25.640000	11.237908	1.322355	12.500000	130.500000	16755500000.000000	13654000000.000000	3.295000	2791829362.100000	13.649696	1.508484	2
7	327.006671	21.917380	2.029752	4.000000	106.000000	698240666.666667	287547000.000000	0.750000	366763235.300000	400.989188	-5.322376	3

	Linkage	Cophenetic Coefficient
0	single	0.934553
1	complete	0.845293
2	average	0.949582
3	centroid	0.947172
4	ward	0.709597
5	weighted	0.915633

	Current Price	Price Change	Volatility	ROE	Cash Ratio	Net Cash Flow	Net Income	Earnings Per Share	Estimated Shares Outstanding	P_E Ratio	P_B Ratio	K_means_segments	count_in_each_segments
HC_Clusters
0	72.526388	4.734948	1.399007	25.315018	53.172161	93306197.802198	1579848003.663004	3.751593	439517549.217985	23.360921	-3.030339	1.989011	273
1	80.825491	16.770902	1.875115	27.588235	339.000000	-65459941.176471	834377058.823529	1.880588	813555933.382353	45.029416	7.898655	1.000000	17
2	37.122609	-16.298641	2.812537	55.086957	49.304348	-429502043.478261	-3019905391.304348	-7.632609	498616643.819130	85.883230	1.216453	3.000000	23
3	429.569995	3.761200	1.565483	18.000000	174.400000	362405800.000000	496954600.000000	9.900000	59200701.450000	55.569866	46.627828	0.000000	5
4	46.672222	5.166566	1.079367	25.000000	58.333333	-3040666666.666667	14848444444.444445	3.435556	4564959946.222222	15.596051	-6.354193	4.000000	9
5	327.006671	21.917380	2.029752	4.000000	106.000000	698240666.666667	287547000.000000	0.750000	366763235.300000	400.989188	-5.322376	7.000000	3
6	25.640000	11.237908	1.322355	12.500000	130.500000	16755500000.000000	13654000000.000000	3.295000	2791829362.100000	13.649696	1.508484	6.000000	2
7	24.485001	-13.351992	3.482611	802.000000	51.000000	-1292500000.000000	-19106500000.000000	-41.815000	519573983.250000	60.748608	1.565141	5.000000	2
8	108.304002	10.737770	1.165694	566.200000	26.600000	-278760000.000000	687180000.000000	1.548000	349607057.720000	34.898915	-16.851358	5.000000	5
9	1274.949951	3.190527	1.268340	29.000000	184.000000	-1671386000.000000	2551360000.000000	50.090000	50935516.070000	25.453183	-1.052429	0.000000	1

	Current Price	Price Change	Volatility	ROE	Cash Ratio	Net Cash Flow	Net Income	Earnings Per Share	Estimated Shares Outstanding	P_E Ratio	P_B Ratio	K_means_segments	count_in_each_segments
HC_Clusters
0	208.557968	13.352255	1.781422	25.565217	296.478261	-42268521.739130	835675782.608696	5.720000	616408256.122609	46.469678	15.928863	0.739130	23
1	84.355716	3.854981	1.827670	633.571429	33.571429	-568400000.000000	-4968157142.857142	-10.841429	398169036.442857	42.284541	-11.589502	5.000000	7
2	37.122609	-16.298641	2.812537	55.086957	49.304348	-429502043.478261	-3019905391.304348	-7.632609	498616643.819130	85.883230	1.216453	3.000000	23
3	46.672222	5.166566	1.079367	25.000000	58.333333	-3040666666.666667	14848444444.444445	3.435556	4564959946.222222	15.596051	-6.354193	4.000000	9
4	72.526388	4.734948	1.399007	25.315018	53.172161	93306197.802198	1579848003.663004	3.751593	439517549.217985	23.360921	-3.030339	1.989011	273
5	327.006671	21.917380	2.029752	4.000000	106.000000	698240666.666667	287547000.000000	0.750000	366763235.300000	400.989188	-5.322376	7.000000	3
6	25.640000	11.237908	1.322355	12.500000	130.500000	16755500000.000000	13654000000.000000	3.295000	2791829362.100000	13.649696	1.508484	6.000000	2